Course Outline
AI in Credit Risk: Foundations and Opportunities for Government
- Comparison of Traditional and AI-Powered Credit Risk Models
- Challenges in Credit Evaluation: Bias, Explainability, and Fairness
- Real-World Case Studies in AI for Lending
Data for Credit Scoring Models for Government
- Sources: Transactional, Behavioral, and Alternative Data
- Data Cleaning and Feature Engineering for Lending Decisions
- Addressing Class Imbalance and Data Scarcity in Risk Prediction
Machine Learning for Credit Scoring for Government
- Logistic Regression, Decision Trees, and Random Forests
- Gradient Boosting (LightGBM, XGBoost) for Enhanced Scoring Accuracy
- Model Training, Validation, and Tuning Techniques
AI-Driven Lending Workflows for Government
- Automating Borrower Segmentation and Loan Risk Assessment
- AI-Enhanced Underwriting and Approval Processes
- Dynamic Pricing and Interest Rate Optimization Using Machine Learning
Model Interpretability and Responsible AI for Government
- Explaining Predictions with SHAP and LIME
- Ensuring Fairness in Credit Models: Bias Detection and Mitigation
- Compliance with Regulatory Frameworks (e.g., ECOA, GDPR)
Generative AI in Lending Scenarios for Government
- Utilizing Large Language Models for Application Review and Document Analysis
- Prompt Engineering for Borrower Communication and Insights
- Synthetic Data Generation for Model Testing
Strategy and Governance for AI in Credit for Government
- Building Internal AI Capabilities versus External Solutions
- Best Practices for Model Lifecycle Management and Governance
- Future Trends: Real-Time Credit Scoring, Open Banking Integration
Summary and Next Steps for Government
Requirements
- A comprehensive understanding of credit risk fundamentals for government and private sector applications
- Experience with data analysis or business intelligence tools, suitable for enhancing decision-making processes
- Familiarity with Python programming language, or a commitment to learning basic syntax to support analytical tasks
Audience
- Lending managers for government and financial institutions
- Credit analysts in both public and private sectors
- Fintech innovators focused on advancing technology solutions for government and industry
Testimonials (3)
The background / theory of LLMs, the exercise
Joanne Wong - IPG HK Limited
Course - Applied AI for Financial Statement Analysis & Reporting
it has opened my mind to new tool that can help me in creating automation
Alessandra Parpajola - Advanced Bionics AG
Course - Machine Learning & AI for Finance Professionals
I very much appreciated the way the trainer presented everything. I understood everything even if Finance is not my area, he made sure that every participant was on the same page, while keeping up with the time left. The exercises were placed at good intervals. Communication with the participants was always there. The material was perfect, not too much, not too little. He elaborated very well on a bit more complicated subjects so that it can be understood by everyone.